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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| IBM Watson Tools and Platform | - Model development and deployment - IBM Watson Studio usage |
| Data Preparation and Analysis | - Exploratory data analysis - Data cleaning and preprocessing - Feature engineering basics |
| Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
| Data Science Fundamentals | - Types of data and data sources - Data science lifecycle |
| Machine Learning Methods | - Unsupervised learning - Model evaluation and validation - Supervised learning |
IBM Watson Data Scientist v1 Sample Questions:
In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?
- A. It ensures that the model is trained on the maximum amount of data possible
- B. It increases the computational complexity without improving model performance
- C. It allows for the model to be validated and tested on different subsets of data to check its generalization ability
- D. It guarantees that the model will perform with 100% accuracy on unseen data
Correct Answer: C 🗳️
Which two graph types are used in EDA to show the relationship between two or more quantitative variables?
- A. Box plot
- B. Scatter plot
- C. Histogram
- D. Stem-and-leaf plot
- E. Heat map
Correct Answer: B,E 🗳️
When anticipating additional data sources that might be relevant, what is a crucial factor to consider?
- A. The graphical interface of the data source
- B. The data source's popularity on social media
- C. The relevance of the data source to the business problem
- D. The color scheme of the data visualization
Correct Answer: C 🗳️
How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?
- A. Data Preparation
- B. Evaluation
- C. Model Building
- D. Business Understanding
Correct Answer: D 🗳️
Which of the following is a key feature of Watson Knowledge Catalog (WKC) for identifying appropriate data sources?
- A. Automated code compilation
- B. Real-time messaging
- C. Data discovery and categorization
- D. Cloud storage optimization
Correct Answer: C 🗳️
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